{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Вам даны результаты проверки двух препаратов. Требуется оценить, есть ли различие в эффекте у этих пациентов.\n",
    "\n",
    "A - результаты для выборки людей получающих препарат А.\n",
    "\n",
    "B - результаты для другой выборки людей, получающих препарат B.\n",
    "\n",
    "Выборки неазвисимы. Представим, что данные в выборках - количество койко-дней в больнице до выздоровления. В этом случае нам необходимо выполнять односторонние тесты, так как интересует только один хвост плотности распределения значений со стороны уменьшения (слева).\n",
    "\n",
    "Будем использовать нормальное распределение данных в выборках и критерий Стьюдента для независимых выборок."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.stats\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Эффективность препаратов одинакова на уровне значимости 5.0%\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(11)\n",
    "A=scipy.stats.norm.rvs(scale=30,loc=11,size=100, random_state=777)\n",
    "B=scipy.stats.norm.rvs(scale=54,loc=11,size=100, random_state=777)\n",
    "alpha = 0.05\n",
    "\n",
    "stat, p = scipy.stats.ttest_ind(A, B, equal_var=False)\n",
    "if p/2 < alpha:\n",
    "    print(f'Эффективность препаратов отличается на уровне значимости {alpha :.1%}')\n",
    "else:\n",
    "    print(f'Эффективность препаратов одинакова на уровне значимости {alpha :.1%}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Вам даны результаты проверки двух препаратов. Требуется оценить, есть ли различие в эффекте у этих пациентов.\n",
    "\n",
    "А - Не принимающие препарат люди\n",
    "\n",
    "B - Люди из группы А, которым дали препарат после проведения исследований без препарата\n",
    "\n",
    "Выборка зависимая. Если наши данные имеют нормальное распределение и количественный тип, то можно использовать параметрический критерий Стьюдента для зависимых выборок. Также для зависимых выборок можно использовать непараметрический критерий Вилкоксона, для него распределение данных не имеет значения.\n",
    "\n",
    "Представим, что данные в выборках - количество койко-дней в больнице до выздоровления. В этом случае нам необходимо выполнять односторонние тесты, так как интересует только один хвост плотности распределения значений со стороны уменьшения (слева).\n",
    "\n",
    "H0 - Эффекта от препарата нет  \n",
    "H1 - Эффект от препарата присутствует"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Эффекта от препарата нет на уровне значимости 5.0%\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(11)\n",
    "A=scipy.stats.norm.rvs(scale=30,loc=11,size=100, random_state=777)\n",
    "B=scipy.stats.norm.rvs(scale=54,loc=11,size=100, random_state=777)\n",
    "alpha = 0.05\n",
    "\n",
    "# Параметрический критерий Стьюдента для зависимыз выборок\n",
    "stat, p = scipy.stats.ttest_rel(A, B)\n",
    "if p/2 < alpha:\n",
    "    print(f'Эффект от препарата присутствует на уровне значимости {alpha :.1%}')\n",
    "else:\n",
    "    print(f'Эффекта от препарата нет на уровне значимости {alpha :.1%}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Эффекта от препарата нет на уровне значимости 5.0%\n"
     ]
    }
   ],
   "source": [
    "# Непараметрический критерий Вилкоксона для зависимыз выборок\n",
    "stat, p = scipy.stats.wilcoxon(A, B, alternative='less')\n",
    "\n",
    "if p < alpha:\n",
    "    print(f'Эффект от препарата присутствует на уровне значимости {alpha :.1%}')\n",
    "else:\n",
    "    print(f'Эффекта от препарата нет на уровне значимости {alpha :.1%}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Допустим вы решили устроить дачный эксперимент. Берем и поливаем одни огурцы водой, другие огурцы водой с удобнением, третью группу огурцов будем поливать минералкой. Используя дисперсионный анализ, сделайте выводы о распредлениях результатов. Если ли эффект от удобрения по сравнению с минералкой? \n",
    "\n",
    "\n",
    "H0 - Эффекты от разного типа поливки не влияют на огурцы  \n",
    "H1 - Эффекты от разного типа поливки оказывают сильное влияние на огурцы\n",
    "\n",
    "Тест двусторонний, выборки независимые. Проверим гипотезы через дисперсионный анализ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Эффекты от разного типа поливки оказывают сильное влияние на огурцы на уровне значимости 5.0%\n"
     ]
    }
   ],
   "source": [
    "water = scipy.stats.norm.rvs(loc=50, scale=20, size=30)\n",
    "nutri = scipy.stats.norm.rvs(loc=80, scale=20, size=30)\n",
    "mineral = scipy.stats.norm.rvs(loc=55, scale=20, size=30)\n",
    "\n",
    "alpha = 0.05\n",
    "\n",
    "stats, p = scipy.stats.f_oneway(water, nutri, mineral)\n",
    "if p < alpha:\n",
    "    print(f'Эффекты от разного типа поливки оказывают сильное влияние на огурцы на уровне значимости {alpha :.1%}')   \n",
    "else:\n",
    "    print(f'Эффекты от разного типа поливки не влияют на огурцы на уровне значимости {alpha :.1%}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Как видим по дисперсионному анализу разные типы поливки огурцов действительно влияют на их параметры. Чтобы установить влияние эффекта от удобрения по сравнению с минералкой можно првоести непараметрический тест U-критерия Манна-Уитни.\n",
    "\n",
    "У критерия есть следующие требования:\n",
    "- в выборках должны быть все числа разные или число повторений должно быть невелико\n",
    "- в каждой из выборок должно быть не менее 3 значений. Если в одной выборке 2 значения, то во второй должно быть не менее 5.\n",
    "\n",
    "H0 - Разница между поливкой огурцов минералкой и удобрениями отсутствует  \n",
    "H1 - Разница между поливкой огурцов минералкой и удобрениями присутствует"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Разница между поливкой огурцов минералкой и удобрениями присутствует на уровне значимости 5.0%\n"
     ]
    }
   ],
   "source": [
    "stats, p = scipy.stats.mannwhitneyu(nutri, mineral)\n",
    "if p < alpha:\n",
    "    print(f'Разница между поливкой огурцов минералкой и удобрениями присутствует на уровне значимости {alpha :.1%}')   \n",
    "else:\n",
    "    print(f'Разница между поливкой огурцов минералкой и удобрениями отсутствует на уровне значимости {alpha :.1%}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Наша статистика имеет нормальное распределение с количественными данными. Поэтому можем применить параметрический критерий Стьюдента для независимых выборок."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Разница между поливкой огурцов минералкой и удобрениями присутствует на уровне значимости 5.0%\n"
     ]
    }
   ],
   "source": [
    "stats, p = scipy.stats.ttest_ind(nutri, mineral)\n",
    "if p < alpha:\n",
    "    print(f'Разница между поливкой огурцов минералкой и удобрениями присутствует на уровне значимости {alpha :.1%}')   \n",
    "else:\n",
    "    print(f'Разница между поливкой огурцов минералкой и удобрениями отсутствует на уровне значимости {alpha :.1%}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Чтобы понять какой вид поливки вносит более высокий вклад в размер огурцов, сравним средние значения"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Поливка огурцов удобрениями оказывает более положительное влияние, чем поливка минералкой на уровне значимости 5.0%\n"
     ]
    }
   ],
   "source": [
    "if nutri.mean() > mineral.mean():\n",
    "    print(f'Поливка огурцов удобрениями оказывает более положительное влияние, чем поливка минералкой \\\n",
    "на уровне значимости {alpha :.1%}')\n",
    "else:\n",
    "    print(f'Поливка огурцов минералкой оказывает более положительное влияние, чем поливка удобрениями \\\n",
    "на уровне значимости {alpha :.1%}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cookie Cats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userid</th>\n",
       "      <th>version</th>\n",
       "      <th>sum_gamerounds</th>\n",
       "      <th>retention_1</th>\n",
       "      <th>retention_7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>116</td>\n",
       "      <td>gate_30</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>337</td>\n",
       "      <td>gate_30</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>377</td>\n",
       "      <td>gate_40</td>\n",
       "      <td>165</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>483</td>\n",
       "      <td>gate_40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>488</td>\n",
       "      <td>gate_40</td>\n",
       "      <td>179</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userid  version  sum_gamerounds  retention_1  retention_7\n",
       "0     116  gate_30               3            0            0\n",
       "1     337  gate_30              38            1            0\n",
       "2     377  gate_40             165            1            0\n",
       "3     483  gate_40               1            0            0\n",
       "4     488  gate_40             179            1            1"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('cookie_cats.csv')\n",
    "data.retention_1 = data.retention_1.apply(lambda x: int(x))\n",
    "data.retention_7 = data.retention_7.apply(lambda x: int(x))\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Данные получены от 90 189 игроков, которые установили игру во время проведения AB-теста. Переменные:\n",
    "\n",
    "userid - уникальный номер, идентифицирующий каждого игрока.\n",
    "\n",
    "версия - был ли игрок помещен в контрольную группу (gate_30 - ворота на уровне 30) или в тестовую группу (gate_40 - ворота на уровне 40).\n",
    "\n",
    "sum_gamerounds - количество игровых раундов, сыгранных игроком в течение первой недели после установки\n",
    "\n",
    "retention_1 - проигрыватель вернулся и поиграл через 1 день после установки?\n",
    "\n",
    "retention_7 - проигрыватель вернулся и играл через 7 дней после установки?\n",
    "\n",
    "Когда игрок устанавливал игру, ему случайным образом назначали gate_30 или gate_40.\n",
    "\n",
    "Разделим наши датафреймы на 2 части:\n",
    "- df_gate_30 -- датафрейм со статистикой ворот на 30 уровне\n",
    "- df_gate_40 -- датафрейм со статистикой ворот на 40 уровне"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_gate_30 = data.loc[data.version == 'gate_30', ['userid', 'sum_gamerounds', 'retention_1', 'retention_7']]\n",
    "df_gate_40 = data.loc[data.version == 'gate_40', ['userid', 'sum_gamerounds', 'retention_1', 'retention_7']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Посмотрим на базовые статистические метрики."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userid</th>\n",
       "      <th>sum_gamerounds</th>\n",
       "      <th>retention_1</th>\n",
       "      <th>retention_7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4.470000e+04</td>\n",
       "      <td>44700.000000</td>\n",
       "      <td>44700.000000</td>\n",
       "      <td>44700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.987564e+06</td>\n",
       "      <td>52.456264</td>\n",
       "      <td>0.448188</td>\n",
       "      <td>0.190201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.881026e+06</td>\n",
       "      <td>256.716423</td>\n",
       "      <td>0.497314</td>\n",
       "      <td>0.392464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.160000e+02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.505469e+06</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.983631e+06</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.481497e+06</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.999710e+06</td>\n",
       "      <td>49854.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             userid  sum_gamerounds   retention_1   retention_7\n",
       "count  4.470000e+04    44700.000000  44700.000000  44700.000000\n",
       "mean   4.987564e+06       52.456264      0.448188      0.190201\n",
       "std    2.881026e+06      256.716423      0.497314      0.392464\n",
       "min    1.160000e+02        0.000000      0.000000      0.000000\n",
       "25%    2.505469e+06        5.000000      0.000000      0.000000\n",
       "50%    4.983631e+06       17.000000      0.000000      0.000000\n",
       "75%    7.481497e+06       50.000000      1.000000      0.000000\n",
       "max    9.999710e+06    49854.000000      1.000000      1.000000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gate_30.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userid</th>\n",
       "      <th>sum_gamerounds</th>\n",
       "      <th>retention_1</th>\n",
       "      <th>retention_7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4.548900e+04</td>\n",
       "      <td>45489.000000</td>\n",
       "      <td>45489.000000</td>\n",
       "      <td>45489.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.009073e+06</td>\n",
       "      <td>51.298776</td>\n",
       "      <td>0.442283</td>\n",
       "      <td>0.182000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.885496e+06</td>\n",
       "      <td>103.294416</td>\n",
       "      <td>0.496663</td>\n",
       "      <td>0.385849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.770000e+02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.517171e+06</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.007329e+06</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.510762e+06</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.999861e+06</td>\n",
       "      <td>2640.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             userid  sum_gamerounds   retention_1   retention_7\n",
       "count  4.548900e+04    45489.000000  45489.000000  45489.000000\n",
       "mean   5.009073e+06       51.298776      0.442283      0.182000\n",
       "std    2.885496e+06      103.294416      0.496663      0.385849\n",
       "min    3.770000e+02        0.000000      0.000000      0.000000\n",
       "25%    2.517171e+06        5.000000      0.000000      0.000000\n",
       "50%    5.007329e+06       16.000000      0.000000      0.000000\n",
       "75%    7.510762e+06       52.000000      1.000000      0.000000\n",
       "max    9.999861e+06     2640.000000      1.000000      1.000000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gate_40.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выводы из анализа базовых статистических метрик:\n",
    "\n",
    "- В обоих выборках значения квартилей практически одинаковы, можно предположить, что глобального влияние расположения ворот на 30 или 40 уровнях нет, можно говорить о небольшом влиянии факторов.\n",
    "- 75% людей на первой неделе наигрывают не более 50-52 раундов.\n",
    "- в обоих выборках только около 25% людей возвращаются в игру через день после установки, а через 7 дней еще меньше.\n",
    "- по средним значениям retention_1 и retention_7 видно, что в случае ворот на 30 уровне люди возвращаются в игру через 1 и 7 дней немножко чаще. Выборсы в значениях отсутствуют, данные у нас порядковые с двумя состояниями, поэтому среднее в данных выборках корректна.\n",
    "- значения медиан сильно расходятся со средними значениями, значит есть выбросы, которые сильно влияют на среднее. Поэтому критерии, основанные на анализе среднего будут не точно работать. Как вариант - разбивать выборку на диапазоны по количеству сыгранных раундов. Например от 0 до 100, от 100 до 1000, от 1000 до 3000.\n",
    "\n",
    "Также наблюдается слишком большая разница между максимальными значениями sum_gamerounds аж более чем на 45 000. Есть предположение, что это выбросы. Проверим какое количество пользователей в выборке df_gate_30 наиграли более 2640 раундов за неделю."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userid</th>\n",
       "      <th>sum_gamerounds</th>\n",
       "      <th>retention_1</th>\n",
       "      <th>retention_7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7912</th>\n",
       "      <td>871500</td>\n",
       "      <td>2961</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57702</th>\n",
       "      <td>6390605</td>\n",
       "      <td>49854</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "        userid  sum_gamerounds  retention_1  retention_7\n",
       "7912    871500            2961            1            1\n",
       "57702  6390605           49854            0            1"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gate_30[df_gate_30.sum_gamerounds > 2640]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Как видно такое аномальное количество уровней дал только один пользователь. Есть подозрение, что это не типичное поведение пользователя, вероятно в игру играл какой-то скрипт. Поэтому это значение можно удалить из выборки или заполнить средним значением."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userid</th>\n",
       "      <th>sum_gamerounds</th>\n",
       "      <th>retention_1</th>\n",
       "      <th>retention_7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4.470000e+04</td>\n",
       "      <td>44700.000000</td>\n",
       "      <td>44700.000000</td>\n",
       "      <td>44700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.987564e+06</td>\n",
       "      <td>51.342135</td>\n",
       "      <td>0.448188</td>\n",
       "      <td>0.190201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.881026e+06</td>\n",
       "      <td>102.056456</td>\n",
       "      <td>0.497314</td>\n",
       "      <td>0.392464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.160000e+02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.505469e+06</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.983631e+06</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.481497e+06</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.999710e+06</td>\n",
       "      <td>2961.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             userid  sum_gamerounds   retention_1   retention_7\n",
       "count  4.470000e+04    44700.000000  44700.000000  44700.000000\n",
       "mean   4.987564e+06       51.342135      0.448188      0.190201\n",
       "std    2.881026e+06      102.056456      0.497314      0.392464\n",
       "min    1.160000e+02        0.000000      0.000000      0.000000\n",
       "25%    2.505469e+06        5.000000      0.000000      0.000000\n",
       "50%    4.983631e+06       17.000000      0.000000      0.000000\n",
       "75%    7.481497e+06       50.000000      1.000000      0.000000\n",
       "max    9.999710e+06     2961.000000      1.000000      1.000000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gate_30.loc[df_gate_30.sum_gamerounds == 49854, 'sum_gamerounds'] = df_gate_30.sum_gamerounds.mean()\n",
    "df_gate_30.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Как видим у нас выровнялись значения дисперсии, теперь они практически равны, а ранее отличались более чем в 2 раза. При этом практически не имзенились другие основные метрики.\n",
    "\n",
    "Проверим нет ли пропусков в наших данных."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 44700 entries, 0 to 90186\n",
      "Data columns (total 4 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   userid          44700 non-null  int64  \n",
      " 1   sum_gamerounds  44700 non-null  float64\n",
      " 2   retention_1     44700 non-null  int64  \n",
      " 3   retention_7     44700 non-null  int64  \n",
      "dtypes: float64(1), int64(3)\n",
      "memory usage: 1.7 MB\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 45489 entries, 2 to 90188\n",
      "Data columns (total 4 columns):\n",
      " #   Column          Non-Null Count  Dtype\n",
      "---  ------          --------------  -----\n",
      " 0   userid          45489 non-null  int64\n",
      " 1   sum_gamerounds  45489 non-null  int64\n",
      " 2   retention_1     45489 non-null  int64\n",
      " 3   retention_7     45489 non-null  int64\n",
      "dtypes: int64(4)\n",
      "memory usage: 1.7 MB\n"
     ]
    }
   ],
   "source": [
    "df_gate_30.info()\n",
    "df_gate_40.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Как видим пропуски в наших данных отсутствуют, поэтому проблема с заполнением отсутствующих данных отсутствует.\n",
    "\n",
    "Посмотрим на плотности распределения наших значений в выборках."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_gate_30['sum_gamerounds'].plot(kind='hist')\n",
    "plt.title(\"Плотность распределения кол-ва сыгранных раундов в первую неделю для ворот на 30 уровне\")\n",
    "plt.ylabel(\"Кол-во пользователей\")\n",
    "plt.xlabel(\"Кол-во сыгранных раунов первой недели\")\n",
    "plt.show()\n",
    "\n",
    "df_gate_30.loc[df_gate_30.sum_gamerounds < df_gate_30.sum_gamerounds.quantile(0.997), 'sum_gamerounds'].plot(kind='hist')\n",
    "plt.title(\"Плотность распределения кол-ва сыгранных раундов в первую неделю для ворот на 40 уровне без выбросов\")\n",
    "plt.ylabel(\"Кол-во пользователей\")\n",
    "plt.xlabel(\"Кол-во сыгранных раунов первой недели\")\n",
    "plt.show()\n",
    "\n",
    "df_gate_40['sum_gamerounds'].plot(kind='hist')\n",
    "plt.title(\"Плотность распределения кол-ва сыгранных раундов в первую неделю для ворот на 40 уровне\")\n",
    "plt.xlabel(\"Кол-во сыгранных раунов первой недели\")\n",
    "plt.show()\n",
    "\n",
    "df_gate_40.loc[df_gate_40.sum_gamerounds < df_gate_40.sum_gamerounds.quantile(0.997), 'sum_gamerounds'].plot(kind='hist')\n",
    "plt.title(\"Плотность распределения кол-ва сыгранных раундов в первую неделю для ворот на 40 уровне без выбросов\")\n",
    "plt.xlabel(\"Кол-во сыгранных раунов первой недели\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Как видно большая часть значений концентрируется на уровне от 0 до 100. Все остальные значения встречаются реже, как сказано было ранее, в боевой задаче имеет смысл значения больше 100 рассматривать отдельной группой.\n",
    "\n",
    "Посмотрим на распределение количества игроков относительно количества сыгранных раундов."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Подсчет количества игроков для каждого количества раундов\n",
    "df_plot_40 = df_gate_40.groupby('sum_gamerounds')['userid'].count()\n",
    "df_plot_40.hist(bins=50)\n",
    "plt.show()\n",
    "\n",
    "df_plot_30 = df_gate_30.groupby('sum_gamerounds')['userid'].count()\n",
    "df_plot_30.hist(bins=50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Как видно по гистограмме большинство игроков играют не больше 50-70 раундов, это было понятно еще на этапе анализа квартилей, медиан и средних."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Построим график распределения игроков, сыгравших от 0 до 100 игровых раундов в течение своей первой недели игры.\n",
    "%matplotlib inline\n",
    "\n",
    "df_plot_30[:100].plot(figsize=(10,6), c='r')\n",
    "plt.title(\"The number of players that played 0-100 game rounds during the first week\")\n",
    "plt.ylabel(\"Number of Players\")\n",
    "plt.xlabel('# Game rounds')\n",
    "\n",
    "df_plot_40[:100].plot(figsize=(10,6), c='g')\n",
    "plt.title(\"The number of players that played 0-100 game rounds during the first week\")\n",
    "plt.ylabel(\"Number of Players\")\n",
    "plt.xlabel('# Game rounds')\n",
    "plt.legend(['Пользователи группы ворот на 30 уровне', 'Пользователи группы ворот на 40 уровне'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Как видно по графику отличия незначительны, но тем не менее есть. Требуется выяснить являются ли эти отличия дейсвтительно статистически значимыми.\n",
    "\n",
    "Ранее в анализе средних значений мы заметили, что пользователи из группы с воротами на 30 уровне возвращаются в игру через день и неделю немножко чаще, чем пользователи с воротами на 40 уровне.\n",
    "\n",
    "Попробуем проверить эту гипотезу с помощью бутстрапа порядковых значений retention_1 и retention_7. С помощью бутстрапа получится воссоздать распределение значений на основе средних."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2409b4aa7f0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating an list with bootstrapped means for each AB-group\n",
    "boot_1d = []\n",
    "for i in range(1000):\n",
    "    boot_mean = data.sample(frac = 1,replace = True).groupby('version')['retention_1'].mean()  #retention_1\n",
    "    boot_1d.append(boot_mean)\n",
    "    \n",
    "# Преобразование списка в DataFrame\n",
    "boot_1d = pd.DataFrame(boot_1d)\n",
    "    \n",
    "# A Kernel Density Estimate plot of the bootstrap distributions\n",
    "boot_1d.plot(kind='density')\n",
    "\n",
    "# Creating an list with bootstrapped means for each AB-group\n",
    "boot_1d = []\n",
    "for i in range(1000):\n",
    "    boot_mean = data.sample(frac = 1,replace = True).groupby('version')['retention_7'].mean()  #retention_1\n",
    "    boot_1d.append(boot_mean)\n",
    "    \n",
    "# Преобразование списка в DataFrame\n",
    "boot_1d = pd.DataFrame(boot_1d)\n",
    "    \n",
    "# A Kernel Density Estimate plot of the bootstrap distributions\n",
    "boot_1d.plot(kind='density')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Как видно по построенным графикам средние значения для пользователей с воротами на 30 уровне смещены в правую часть, а это означает, что именно эти пользователи возвращаются в игру немного чаще, чем пользователи с воротами на 40 уровне.\n",
    "\n",
    "Теперь проверим гипотезы о влиянии ворот на разных уровнях на вовлеченность пользователей. Дисперсионный анализ мы не можем использовать, так как у нас даныне не имеют нормального распределения и даже не приближены к нему. Плюс \n",
    "\n",
    "H0 - Ворота на разных уровнях не влияют на количество пройденных уровней пользователями в первую неделю  \n",
    "H1 - Ворота на разных уровнях оказывают значимое влияние на количество пройденных уровней пользователями в первую неделю\n",
    "\n",
    "Так как у нас данные распределены не нормально и есть значительные выбросы, то для проверки этой гипотезы больше всего подходит U-критерий Манна-Уитни для независимых выборок. нас интересуют отклонения в обе стороны распределения, поэтому тест будет двусторонним."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value: 0.025\n",
      "Ворота на разных уровнях оказывают значимое влияние на количество пройденных уровней пользователями в первую неделю на уровне значимости 5.00%\n",
      "Ворота на уровне 30 больше затягивают пользователей в процесс игры\n"
     ]
    }
   ],
   "source": [
    "stat, p = scipy.stats.mannwhitneyu(df_gate_30.sum_gamerounds, df_gate_40.sum_gamerounds)\n",
    "alpha = 0.05\n",
    "\n",
    "print('p-value:', round(p, 3))\n",
    "\n",
    "if p < alpha:\n",
    "    print(f'Ворота на разных уровнях оказывают значимое влияние на количество пройденных уровней пользователями \\\n",
    "в первую неделю на уровне значимости {alpha :.2%}')\n",
    "    if df_gate_30.sum_gamerounds.mean() > df_gate_40.sum_gamerounds.mean():\n",
    "        print(f'Ворота на уровне 30 больше затягивают пользователей в процесс игры')\n",
    "    else:\n",
    "        print(f'Ворота на уровне 40 больше затягивают пользователей в процесс игры')     \n",
    "else:\n",
    "    print(f'Ворота на разных уровнях не влияют на количество пройденных уровней пользователями в первую неделю \\\n",
    "на уровне значимости {alpha :.2%}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Так как в наших данных было много выбросов и абволютное большинство пользователей не наигрывают более 100 раундов в первую неделю, то во избежания ошибки в оценке наибольшего влияния сделаем пересчет."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ворота на уровне 30 больше затягивают пользователей в процесс игры\n"
     ]
    }
   ],
   "source": [
    "if (df_gate_30[df_gate_30.sum_gamerounds < 100].sum_gamerounds.mean() > \n",
    "    df_gate_40[df_gate_40.sum_gamerounds < 100].sum_gamerounds.mean()):\n",
    "    print(f'Ворота на уровне 30 больше затягивают пользователей в процесс игры')\n",
    "else:\n",
    "    print(f'Ворота на уровне 40 больше затягивают пользователей в процесс игры')    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Как видим дейсвтительно ворота на 30 уровне оказывают большую вовлеченность пользователей в плане количества сыгранных ими раундов.\n",
    "\n",
    "Аналогично проверим параметры retention_1 и retention_2. Для них точно также больше всего подходит U-критерий Манна-Уитни, так как данные имеют порядковый характер и выборки независимые.\n",
    "\n",
    "Сформулируем гипотезы для retention_1:\n",
    "\n",
    "    H0 - Пользователи возвращаются в игру спустя сутки одинаково и независимо от наличия ворот на уровне 30 или 40  \n",
    "    H1 - Количество возвратов в игру спустя сутки зависит от наличия ворот либо на 30 уровне, либо на 40  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value: 0.037\n",
      "Количество возвратов в игру спустя сутки зависит от наличия ворот либо на 30 уровне, либо на 40 на уровне значимости 5.00%\n",
      "Количество возвратов в игру спустя сутки больше при использовании ворот на уровне 30\n"
     ]
    }
   ],
   "source": [
    "stat, p = scipy.stats.mannwhitneyu(df_gate_30.retention_1, df_gate_40.retention_1)\n",
    "alpha = 0.05\n",
    "\n",
    "print('p-value:', round(p, 3))\n",
    "\n",
    "if p < alpha:\n",
    "    print(f'Количество возвратов в игру спустя сутки зависит от наличия ворот либо на 30 уровне, либо на 40 \\\n",
    "на уровне значимости {alpha :.2%}')\n",
    "    if df_gate_30.retention_1.mean() > df_gate_40.retention_1.mean():\n",
    "        print(f'Количество возвратов в игру спустя сутки больше при использовании ворот на уровне 30')\n",
    "    else:\n",
    "        print(f'Количество возвратов в игру спустя сутки больше при использовании ворот на уровне 40')     \n",
    "else:\n",
    "    print(f'Пользователи возвращаются в игру спустя сутки одинаково и независимо от наличия ворот на уровне 30 или 40 \\\n",
    "на уровне значимости {alpha :.2%}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Сформулируем гипотезы для retention_7:\n",
    "\n",
    "    H0 - Пользователи возвращаются в игру спустя 7 дней одинаково и независимо от наличия ворот на уровне 30 или 40  \n",
    "    H1 - Количество возвратов в игру спустя 7 дней зависит от наличия ворот либо на 30 уровне, либо на 40"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value: 0.001\n",
      "Количество возвратов в игру спустя 7 дней зависит от наличия ворот либо на 30 уровне, либо на 40 на уровне значимости 5.00%\n",
      "Количество возвратов в игру спустя 7 дней больше при использовании ворот на уровне 30\n"
     ]
    }
   ],
   "source": [
    "stat, p = scipy.stats.mannwhitneyu(df_gate_30.retention_7, df_gate_40.retention_7)\n",
    "alpha = 0.05\n",
    "\n",
    "print('p-value:', round(p, 3))\n",
    "\n",
    "if p < alpha:\n",
    "    print(f'Количество возвратов в игру спустя 7 дней зависит от наличия ворот либо на 30 уровне, либо на 40 \\\n",
    "на уровне значимости {alpha :.2%}')\n",
    "    if df_gate_30.retention_7.mean() > df_gate_40.retention_7.mean():\n",
    "        print(f'Количество возвратов в игру спустя 7 дней больше при использовании ворот на уровне 30')\n",
    "    else:\n",
    "        print(f'Количество возвратов в игру спустя 7 дней больше при использовании ворот на уровне 40')     \n",
    "else:\n",
    "    print(f'Пользователи возвращаются в игру спустя 7 дней одинаково и независимо от наличия ворот на уровне 30 или 40 \\\n",
    "на уровне значимости {alpha :.2%}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ВЫВОД\n",
    "\n",
    "Отличия вовлеченности пользователей в группах с воротами на 30 уровне и на 40 уровне статистически значимы на уровне 5%.\n",
    "Для бизнеса более положительное влияние на вовлеченность оказывают ворота на 30 уровне, чем на уровне 40."
   ]
  }
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